These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

161 related articles for article (PubMed ID: 39191174)

  • 1. Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study.
    Li Z; Gao J; Zhou H; Li X; Zheng T; Lin F; Wang X; Chu T; Wang Q; Wang S; Cao K; Liang Y; Zhao F; Xie H; Xu C; Zhang H; Niu Q; Ma H; Mao N
    EBioMedicine; 2024 Sep; 107():105311. PubMed ID: 39191174
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
    Gu J; Tong T; Xu D; Cheng F; Fang C; He C; Wang J; Wang B; Yang X; Wang K; Tian J; Jiang T
    Cancer; 2023 Feb; 129(3):356-366. PubMed ID: 36401611
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer.
    Kim R; Chang JM; Lee HB; Lee SH; Kim SY; Kim ES; Cho N; Moon WK
    Radiology; 2019 Oct; 293(1):49-57. PubMed ID: 31407967
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
    Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
    EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.
    Zhou H; Hua Z; Gao J; Lin F; Chen Y; Zhang S; Zheng T; Wang Z; Shao H; Li W; Liu F; Li Q; Chen J; Wang X; Zhao F; Qu N; Xie H; Ma H; Zhang H; Mao N
    J Magn Reson Imaging; 2024 May; 59(5):1710-1722. PubMed ID: 37497811
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.
    Liu S; Du S; Gao S; Teng Y; Jin F; Zhang L
    BMC Cancer; 2023 Jan; 23(1):15. PubMed ID: 36604679
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.
    Yu Y; Chen R; Yi J; Huang K; Yu X; Zhang J; Song C
    Breast; 2024 Oct; 77():103786. PubMed ID: 39137488
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting nodal status using dynamic contrast-enhanced magnetic resonance imaging in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy with and without sequential trastuzumab.
    Hsiang DJ; Yamamoto M; Mehta RS; Su MY; Baick CH; Lane KT; Butler JA
    Arch Surg; 2007 Sep; 142(9):855-61; discussion 860-1. PubMed ID: 17875840
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.
    Gao J; Zhong X; Li W; Li Q; Shao H; Wang Z; Dai Y; Ma H; Shi Y; Zhang H; Duan S; Zhang K; Yang P; Zhao F; Zhang H; Xie H; Mao N
    J Magn Reson Imaging; 2023 Jun; 57(6):1842-1853. PubMed ID: 36219519
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models.
    Lin Y; Wang J; Li M; Zhou C; Hu Y; Wang M; Zhang X
    Breast; 2024 Aug; 76():103737. PubMed ID: 38696854
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Nomogram for predicting axillary lymph node pathological response in node-positive breast cancer patients after neoadjuvant chemotherapy.
    Wang W; Wang X; Liu J; Zhu Q; Wang X; Wang P
    Chin Med J (Engl); 2021 Dec; 135(3):333-340. PubMed ID: 35108228
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer: A Multicenter Retrospective Study.
    Huang X; Shi Z; Mai J; Liu C; Liu C; Chen S; Lu H; Li Y; He B; Li J; Cun H; Han C; Chen X; Liang C; Liu Z
    Acad Radiol; 2023 Jul; 30(7):1257-1269. PubMed ID: 36280517
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A model to predict pathologic complete response of axillary lymph nodes to neoadjuvant chemo(immuno)therapy in patients with clinically node-positive breast cancer.
    Schipper RJ; Moossdorff M; Nelemans PJ; Nieuwenhuijzen GA; de Vries B; Strobbe LJ; Roumen RM; van den Berkmortel F; Tjan-Heijnen VC; Beets-Tan RG; Lobbes MB; Smidt ML
    Clin Breast Cancer; 2014 Oct; 14(5):315-22. PubMed ID: 24548732
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.
    Li Z; Ma Q; Gao Y; Qu M; Li J; Lei J
    Eur Radiol; 2024 Feb; 34(2):930-942. PubMed ID: 37615764
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Construction of a nomogram prediction model for pathological complete response (pCR) of ipsilateral supraclavicular lymph node after neoadjuvant chemotherapy for breast cancer with first diagnosis of ipsilateral supraclavicular lymph node metastasis].
    Lyu MH; Jiao DC; Wu JZ; Tian PQ; Ma YZ; Liu ZZ; Chen XC
    Zhonghua Zhong Liu Za Zhi; 2022 Feb; 44(2):160-166. PubMed ID: 35184460
    [No Abstract]   [Full Text] [Related]  

  • 16. The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.
    Wang J; Tian C; Zheng BJ; Zhang J; Jiao DC; Qu JR; Liu ZZ
    BMC Cancer; 2024 May; 24(1):549. PubMed ID: 38693523
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.
    Zhu T; Huang YH; Li W; Zhang YM; Lin YY; Cheng MY; Wu ZY; Ye GL; Lin Y; Wang K
    Int J Surg; 2023 Nov; 109(11):3383-3394. PubMed ID: 37830943
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years.
    Qian L; Liu X; Zhou S; Zhi W; Zhang K; Li H; Li J; Chang C
    Front Endocrinol (Lausanne); 2024; 15():1323452. PubMed ID: 39072273
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.
    Zhang B; Yu Y; Mao Y; Wang H; Lv M; Su X; Wang Y; Li Z; Zhang Z; Bian T; Wang Q
    Acad Radiol; 2024 Mar; 31(3):800-811. PubMed ID: 37914627
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation.
    Zheng Q; Yan H; He Y; Wang J; Zhang N; Huo L; Liu Y; Wang L; Xu L; Fan Z
    Cancer; 2024 Apr; 130(S8):1513-1523. PubMed ID: 38427584
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.